Once images are assigned to clusters, we desire to identify discriminative regions from each image to understand the components in the image that lead to its cluster assignment. This understanding is especially important when the cluster assignments may be used to inform decision-making or create new disease sub-types, and clinicians want to better understand the clustering results. Various methods have been developed to identify discriminative localizations on images for classification [1]}, [2]}, [3]}. In this work, we modify Score-CAM [3]}, due to its success for classification, to identify discriminative regions specifically for clustering.
